American Airlines Data Engineer interviews, per two candidate reports, center on two technical rounds covering PySpark, SQL window functions and CTEs, Azure CI/CD, and deep dives into real project decisions.
$107K
Avg. Base Comp
$120K
Avg. Total Comp
Not reported
Typical Rounds
Not reported
Process Length
American Airlines' Data Engineer interview process, based on two candidate reports, typically runs through two technical rounds that lean heavily on PySpark and SQL problem-solving. One candidate described two moderate-difficulty technical rounds covering Python, PySpark, SQL, Azure Data Factory, CI/CD, Agile sprint habits, and lead experience. Their questions included reduceByKey versus groupByKey, PySpark window functions, DENSE_RANK, and a recursive SQL CTE on managers and their reportees. A second candidate reported a recruiter screening call about background and experience first, followed by two roughly hour-long technical rounds with three panelists present across the sessions. That candidate spent about 45 minutes on challenging SQL and PySpark coding. They also fielded questions on Redis clusters and configuration, Azure DevOps pipelines, API design, and a programming-fundamentals question about writing jagged numbers from a list. The second candidate also said interviewers dug deeply into actual project work, asking not just what was built but why specific technical decisions were made. Outcomes differed between the two reports — one candidate received an offer, the other did not. Preparation should emphasize explaining the reasoning behind your own projects' technical choices, alongside drilling SQL window functions and PySpark transformations. This guide draws on only two reports, so treat the exact round count and timing as a general guide rather than a fixed script.
Synthesized from 2 candidate reports by our editorial team.
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Real interview reports from people who went through the American Airlines process.
I went through a screening round first where the recruiter asked about my background and experience. After that, I had two technical interview rounds scheduled for about an hour each, with three panelists present across the sessions. The interviewers spent a lot of time diving into my project experience, asking detailed questions about what I'd actually built and the decisions I made. They wanted to understand not just the what, but the why behind my technical choices.
The technical questions were heavy on SQL and PySpark coding problems. These weren't easy — they took up roughly 45 minutes of the interview and felt genuinely challenging. I got questions on data manipulation in PySpark and some complex SQL work that required thinking through optimization. Beyond that, they asked about Redis clusters and configurations, Azure DevOps pipelines and CI/CD practices, and API design questions. There was also a question about writing jagged numbers from a list, which was more of a programming fundamentals check. The interviewers seemed to be testing both my hands-on coding ability and my understanding of data engineering infrastructure and deployment practices.
Prep tip from this candidate
Focus heavily on PySpark and SQL optimization problems — 45 minutes of technical time is dedicated to those, so practice complex data transformation queries and PySpark operations under time pressure. Also prepare concrete examples from your own projects because they dig deep into what you've actually shipped, so have a few stories ready where you can explain architectural decisions, trade-offs, and lessons learned.
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Sourced from candidate reports and verified by our team.
Topics based on recent interview experiences.
| Question | |
|---|---|
| Top 3 Users | |
| Hurdles In Data Projects | |
| Classification and Regression | |
| Time on FB Distribution | |
| Boarding Times Bias | |
| International e-Commerce Warehouse | |
| Text Editor With OOP | |
| Cloud-Agnostic Deployments | |
| Client Solution Pushback | |
| Distributed Authentication Model | |
| Your Strengths and Weaknesses | |
| Flight Routes - 2 | |
| Vision Setting and Execution Strategy | |
| Stakeholder Communication | |
| Data Cleaning Experiences | |
| Measuring Customer Service Quality | |
| Lifetime Driver | |
| Empty Neighborhoods | |
| 2nd Highest Salary | |
| Rolling Bank Transactions | |
| Comments Histogram | |
| Closest SAT Scores | |
| Employee Salaries | |
| Subscription Overlap | |
| Top Three Salaries | |
| Merge Sorted Lists | |
| Cumulative Distribution | |
| Experiment Validity | |
| Download Facts |
Synthesized from candidate reports. Individual experiences may vary.
One candidate reports the process opening with a recruiter screen focused on background and experience before any technical rounds were scheduled. The other candidate's account did not mention this step, so it may apply inconsistently depending on the specific requisition or team.
One candidate reports two moderate technical rounds covering Python, PySpark, and SQL alongside Azure Data Factory and CI/CD topics. Their questions included reduceByKey versus groupByKey, PySpark window functions, DENSE_RANK, PySpark performance optimization, and a recursive SQL CTE problem on managers and their number of reportees. They were also asked how they work within one-week Agile sprints.
Another candidate described roughly hour-long technical rounds, with about 45 minutes dedicated to SQL and PySpark coding problems they found genuinely challenging. These included data manipulation in PySpark and SQL work requiring optimization thinking. The same candidate reported questions on Redis clusters and configuration, Azure DevOps pipelines and CI/CD practices, API design, and a programming-fundamentals question about writing jagged numbers from a list.
One candidate noted that interviewers spent significant time probing actual project experience. They asked detailed questions about what was built and the reasoning behind specific technical choices, wanting the why behind decisions rather than surface-level descriptions. Having concrete examples ready may help.
Of the two reported outcomes, one candidate received an offer after the technical rounds while the other did not. Neither report specifies the end-to-end timeline from application to decision, so applicants should not assume a fixed total process length based on this evidence alone.
